Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add fabioc-aloha/Alex_Skill_Mall --skill agent-memory-architecturegit clone --depth 1 https://github.com/fabioc-aloha/Alex_Skill_MallWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/agent-memory-architecture)<a href="https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/agent-memory-architecture"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/agent-memory-architecture/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/agent-memory-architecture"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/agent-memory-architecture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00068 | $0.03284 |
| Opus 5 | $0.00034 | $0.01642 |
| Sonnet 5 | $0.00014 | $0.00657 |
| Haiku 4.5 | $0.00007 | $0.00328 |
Grade A, and why
memory-systems scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to memory-systems — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory System Design
Memory provides the persistence layer that allows agents to maintain continuity across sessions and reason over accumulated knowledge. Simple agents rely entirely on context for memory, losing all state when sessions end. Sophisticated agents implement layered memory architectures that balance immediate context needs with long-term knowledge retention. The evolution from vector stores to knowledge graphs to temporal knowledge graphs represents increasing investment in structured memory for improved retrieval and reasoning.
When to Activate
Activate this skill when:
- Building agents that must persist knowledge across sessions
- Choosing between memory frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee)
- Needing to maintain entity consistency across conversations
- Implementing reasoning over accumulated knowledge
- Designing memory architectures that scale in production
- Evaluating memory systems against benchmarks (LoCoMo, LongMemEval, DMR)
- Building dynamic memory with automatic entity/relationship extraction and self-improving memory (Cognee)
Do not activate this skill for adjacent work owned by other skills:
- File-backed scratchpads, run logs, and tool-output offloading:
filesystem-context. - Conversation compaction or human-readable handoff summaries:
context-compression. - Masking, prefix caching, token budgets, or retrieval scoping inside one trajectory:
context-optimization. - Formal belief/desire/intention models over RDF state:
bdi-mental-states.
Core Concepts
Think of memory as a spectrum from volatile context window to persistent storage. Default to the simplest layer that meets retrieval needs, because benchmark evidence suggests tool complexity matters less than reliable retrieval for some memory workloads (claim-memory-locomo-filesystem-baseline). Add structure (graphs, temporal validity) only when retrieval quality degrades or the agent needs multi-hop reasoning, relationship traversal, or time-travel queries.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 230 lines · 68 tokens per session scan A 7285a94441b9
memory-systems is a skill published in the GitHub repository fabioc-aloha/Alex_Skill_Mall (4 stars, last pushed 3d ago), licensed MIT. It adds 68 tokens to every session and 3,284 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to memory-systems, differing in 6 lines, and is treated as a copy.
Other skills, from other repositories
mem0-integration
Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.
wegent-knowledge
Knowledge base management and search tools for Wegent. Provides capabilities to list, create, update, and search knowledge bases and documents using RAG retrieval. Use this skill when the user wants to manage knowledge bases, documents, or search for information programmatically.
vector-memory
HNSW vector search for pattern similarity retrieval and knowledge graph maintenance with PageRank scoring, community detection, and 3-tier memory management.
karpathy-llm-wiki
Use when building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.
world-model-diagnostic
Twenty-minute diagnostic mapping a team to a world-model paradigm (vector DB, structured ontology, signal-fidelity). Use when you say "run the world model diagnostic", "audit our world model", "which world model architecture fits us", or "audit where we automate judgment". Use for AI readiness assessments and…
ai-orchestration-langchain
LangChain.js patterns for building LLM applications — chat models, LCEL chains, prompt templates, structured output, agents, tools, RAG, streaming, and LangSmith tracing.